Machine Learning for Trading

关键信息

credit_card免费进入

关于内容

This course introduces students to the real world challenges of implementing machine learning based trading strategies including the algorithmic steps from information gathering to market orders. The focus is on how to apply probabilistic machine learning approaches to trading decisions. We consider statistical approaches like linear regression, KNN and regression trees and how to apply them to actual stock trading situations.

课程大纲

This course is composed of three _mini-courses_: - Mini-course 1: Manipulating Financial Data in Python - Mini-course 2: Computational Investing - Mini-course 3: Machine Learning Algorithms for Trading Each mini-course consists of about 7-10 short lessons. Assignments and projects are interleaved. **Fall 2015 OMS students**: There will be two tests - one midterm after mini-course 2, and one final exam.

record_voice_over

教师

Tucker Balch - Tucker is a former USAF F-15 pilot and current professor of Interactive Computing at the Georgia Institute of Technology. Dr. Balch’s research centers on Machine Learning. He teaches courses in multi-robot systems, Artificial Intelligence and Finance. Balch has published over 120 research publications. His work has been covered by CNN, Institutional Investor, the Wall Street Journal and the New York Times. Balch earned a Bachelor’s degree at Georgia Tech, a Master’s degree at UC Davis, and a Ph.D. at Georgia Tech. His graduated students work at NASA JPL, CMU, Uber, Goldman Sachs, Morgan Stanley, Citadel, AQR, and Yahoo! Finance.

store

内容设计师

The Georgia Institute of Technology is one of the nation's top research universities, distinguished by its commitment to improving the human condition through advanced science and technology.
Georgia Tech's campus occupies 400 acres in the heart of the city of Atlanta, where more than 20,000 undergraduate and graduate students receive a focused, technologically based education.